Robust Design-Based Estimation and Inference for Stratified Randomized Trials with Varying Cluster Sizes
群组规模不等的分层随机试验:稳健设计估计与推断
Clustered randomized controlled trials are often stratified or pair-matched to improve covariate balance and efficiency. Sample average treatment effects (SATEs) are commonly estimated by averaging stratum-level treatment-control mean contrasts -- an approach that is natural and widely used. We show that, in stratified clustered trials with heterogeneous cluster sizes, such estimators need not be consistent for the SATE. They can converge to the wrong limit even under…